Context-Aware Recommender System for Location-Based Advertising


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Demand for context-aware systems continues to grow due to the diffusion of mobile devices. This trend may represent good market opportunities for mobile service industries. Thus, context-aware or location-based advertising (LBA) has been an interesting marketing tool for many companies. However, some studies reported that the performance of context-aware marketing or advertising has been quite disappointing. In this study, we propose a novel context-aware recommender system for LBA. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user’s needs type. In particular, we employ a classification rule to understand user’s needs type using a decision tree algorithm. We empirically validated the effectiveness of the proposed model by using a real-world dataset. Experimental results show that our model makes more accurate and satisfactory advertisements than comparative systems.



Key Engineering Materials (Volumes 467-469)

Edited by:

Dehuai Zeng






H. C. Ahn and K. J. Kim, "Context-Aware Recommender System for Location-Based Advertising", Key Engineering Materials, Vols. 467-469, pp. 2091-2096, 2011

Online since:

February 2011




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